{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inductive node classification and representation learning using GraphSAGE\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/node-classification/graphsage-inductive-node-classification.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/node-classification/graphsage-inductive-node-classification.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates inductive representation learning and node classification using the GraphSAGE [1] algorithm applied to inferring the subject of papers in a citation network.\n",
    "\n",
    "To demonstrate inductive representation learning, we train a GraphSAGE model on a subgraph of the Pubmed-Diabetes citation network. Next, we use the trained model to predict the subject of nodes that were excluded from the subgraph used for model training. \n",
    "\n",
    "We remove 20 percent of the network nodes (including all the edges from these nodes to any other nodes in the network) and then train a GraphSAGE model using this network comprised of the remaining 80 percent of nodes. For training, we only use 5 percent of labeled data.\n",
    "\n",
    "After training the model, we use it to predict the labels, i.e., paper subjects, of the nodes originally held out after re-inserting them in the network. For prediction, we do not retrain the GraphSAGE model.\n",
    "\n",
    "**References**\n",
    "\n",
    "[1] Inductive Representation Learning on Large Graphs. W.L. Hamilton, R. Ying, and J. Leskovec arXiv:1706.02216 [cs.SI], 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "outputs": [],
   "source": [
    "# install StellarGraph if running on Google Colab\n",
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "  %pip install -q stellargraph[demos]==1.2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "VersionCheck"
    ]
   },
   "outputs": [],
   "source": [
    "# verify that we're using the correct version of StellarGraph for this notebook\n",
    "import stellargraph as sg\n",
    "\n",
    "try:\n",
    "    sg.utils.validate_notebook_version(\"1.2.1\")\n",
    "except AttributeError:\n",
    "    raise ValueError(\n",
    "        f\"This notebook requires StellarGraph version 1.2.1, but a different version {sg.__version__} is installed.  Please see <https://github.com/stellargraph/stellargraph/issues/1172>.\"\n",
    "    ) from None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "import os\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph import globalvar\n",
    "from stellargraph.mapper import GraphSAGENodeGenerator\n",
    "from stellargraph.layer import GraphSAGE\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "DataLoadingLinks"
    ]
   },
   "source": [
    "(See [the \"Loading from Pandas\" demo](../basics/loading-pandas.ipynb) for details on how data can be loaded.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "DataLoading"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "The PubMed Diabetes dataset consists of 19717 scientific publications from PubMed database pertaining to diabetes classified into one of three classes. The citation network consists of 44338 links. Each publication in the dataset is described by a TF/IDF weighted word vector from a dictionary which consists of 500 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.PubMedDiabetes()\n",
    "display(HTML(dataset.description))\n",
    "graph_full, labels = dataset.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StellarGraph: Undirected multigraph\n",
      " Nodes: 19717, Edges: 44338\n",
      "\n",
      " Node types:\n",
      "  paper: [19717]\n",
      "    Features: float32 vector, length 500\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [44338]\n"
     ]
    }
   ],
   "source": [
    "print(graph_full.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aim to train a graph-ML model that will predict the \"label\" attribute on the nodes. These labels are one of 3 categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 2, 3}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to **remove 20 percent of the nodes from the graph**. Then, we are going to train a GraphSAGE model on the reduced graph with the remaining 80 percent of the nodes from the original graph. Later, we are going to re-introduce the removed nodes and try to predict their labels without re-training the GraphSAGE model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19717"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_sampled = labels.sample(frac=0.8, replace=False, random_state=101)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`labels_sampled` is a Series that holds the node IDs (the series index) and the associated label of the each of the nodes in the subgraph we are going to use for training the GraphSAGE model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15774"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(labels_sampled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we are going to extract the subgraph corresponding to the sampled nodes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_sampled = graph_full.subgraph(labels_sampled.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StellarGraph: Undirected multigraph\n",
      " Nodes: 15774, Edges: 28852\n",
      "\n",
      " Node types:\n",
      "  paper: [15774]\n",
      "    Features: float32 vector, length 500\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [28852]\n"
     ]
    }
   ],
   "source": [
    "print(graph_sampled.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note above that both the number of nodes and edges have been reduced after removing 20 percent of the nodes in the original graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for testing. We'll use scikit-learn to do this.\n",
    "\n",
    "We are going to use 5 percent of the data for training and of the remaining nodes, we are going to use 20 percent as the validation set. The other 80 percent we can treat as a test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels, test_labels = model_selection.train_test_split(\n",
    "    labels_sampled,\n",
    "    train_size=0.05,\n",
    "    test_size=None,\n",
    "    stratify=labels_sampled,\n",
    "    random_state=42,\n",
    ")\n",
    "val_labels, test_labels = model_selection.train_test_split(\n",
    "    test_labels, train_size=0.2, test_size=None, stratify=test_labels, random_state=100,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note using stratified sampling gives the following counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({1: 164, 3: 310, 2: 314})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "Counter(train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = preprocessing.LabelBinarizer()\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_labels)\n",
    "val_targets = target_encoding.transform(val_labels)\n",
    "test_targets = target_encoding.transform(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the GraphSAGE model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To feed data from the graph to the Keras model we need a generator. The generators are specialized to the model and the learning task so we choose the `GraphSAGENodeGenerator` as we are predicting node attributes with a GraphSAGE model.\n",
    "\n",
    "We need two other parameters, the `batch_size` to use for training and the number of nodes to sample at each level of the model. Here we choose a two-layer model with 10 nodes sampled in the first layer, and 10 in the second."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 50\n",
    "num_samples = [10, 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `GraphSAGENodeGenerator` object is required to send the node features in sampled subgraphs to Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = GraphSAGENodeGenerator(graph_sampled, batch_size, num_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `generator.flow()` method, we can create iterators over nodes that should be used to train, validate, or evaluate the model. For training we use only the training nodes returned from our splitter and the target values. The `shuffle=True` argument is given to the `flow` method to improve training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_labels.index, train_targets, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model, we need a few more parameters for this:\n",
    "\n",
    " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 32-dimensional hidden node features at each layer.\n",
    " * The `bias` and `dropout` are internal parameters of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "graphsage_model = GraphSAGE(\n",
    "    layer_sizes=[32, 32], generator=generator, bias=True, dropout=0.5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a model to predict the 3 categories using Keras softmax layers. Note that we need to use the `G.get_target_size` method to find the number of categories in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp, x_out = graphsage_model.in_out_tensors()\n",
    "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([None, 3])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `graph_model` and outputs being the predictions from the softmax layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=x_inp, outputs=prediction)\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.005),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[\"acc\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the validation set (we need to create another generator over the validation data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_gen = generator.flow(val_labels.index, val_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(\n",
    "    train_gen, epochs=15, validation_data=val_gen, verbose=0, shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg.utils.plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have trained the model we can evaluate on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gen = generator.flow(test_labels.index, test_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.5591\n",
      "\tacc: 0.8149\n"
     ]
    }
   ],
   "source": [
    "test_metrics = model.evaluate(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making predictions with the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to use the trained model to predict the nodes we put aside earlier. For this, we must use the original StellarGraph object and a new node generator."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The new generator feeds data from this full graph into the model trained on the sampled partial graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = GraphSAGENodeGenerator(graph_full, batch_size, num_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get the predictions themselves for all nodes in the hold out set. We are going to use an iterator `generator.flow()` over the hold-out nodes for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "hold_out_nodes = labels.index.difference(labels_sampled.index)\n",
    "labels_hold_out = labels[hold_out_nodes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3943"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hold_out_nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "hold_out_targets = target_encoding.transform(labels_hold_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "hold_out_gen = generator.flow(hold_out_nodes, hold_out_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a generator for our hold out data, we can use our trained model to make predictions for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "hold_out_predictions = model.predict(hold_out_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specification to turn these values back to the original categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "hold_out_predictions = target_encoding.inverse_transform(hold_out_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3943"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hold_out_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at a few:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pid</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>7145</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48487</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49051</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>77059</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83020</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95802</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98828</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>98959</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>106628</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>126184</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Predicted  True\n",
       "pid                    \n",
       "7145            1     1\n",
       "48487           2     2\n",
       "49051           1     1\n",
       "77059           2     2\n",
       "83020           2     2\n",
       "95802           2     2\n",
       "98828           3     2\n",
       "98959           2     2\n",
       "106628          2     2\n",
       "126184          1     1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.Series(hold_out_predictions, index=hold_out_nodes)\n",
    "df = pd.DataFrame({\"Predicted\": results, \"True\": labels_hold_out})\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Hold Out Set Metrics:\n",
      "\tloss: 0.5561\n",
      "\tacc: 0.8237\n"
     ]
    }
   ],
   "source": [
    "hold_out_metrics = model.evaluate(hold_out_gen)\n",
    "print(\"\\nHold Out Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, hold_out_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that inductive performance of the model on the hold out nodes (not present in the graph during training) is on par with the performance on the test set of nodes that were present in the graph during training, but whose labels were concealed. This demonstrates good inductive performance of GraphSAGE model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node embeddings for hold out nodes\n",
    "\n",
    "We are going to extract node embeddings as activations of the output of GraphSAGE layer stack, and visualise them, coloring nodes by their subject label.\n",
    "\n",
    "The GraphSAGE embeddings are the output of the GraphSAGE layers, namely the `x_out` variable. Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. Additionally note that the weights trained previously are kept in the new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_model = Model(inputs=x_inp, outputs=x_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3943, 32)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb = embedding_model.predict(hold_out_gen)\n",
    "emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = emb\n",
    "y = np.argmax(target_encoding.transform(labels_hold_out), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "if X.shape[1] > 2:\n",
    "    transform = TSNE  # PCA\n",
    "\n",
    "    trans = transform(n_components=2)\n",
    "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=hold_out_nodes)\n",
    "    emb_transformed[\"label\"] = y\n",
    "else:\n",
    "    emb_transformed = pd.DataFrame(X, index=hold_out_nodes)\n",
    "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
    "    emb_transformed[\"label\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "ax.scatter(\n",
    "    emb_transformed[0],\n",
    "    emb_transformed[1],\n",
    "    c=emb_transformed[\"label\"].astype(\"category\"),\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\n",
    "    \"{} visualization of GraphSAGE embeddings of hold out nodes for pubmed dataset\".format(\n",
    "        transform.__name__\n",
    "    )\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrated inductive representation learning and node classification using the GraphSAGE algorithm.\n",
    "\n",
    "More specifically, the notebook demonstrated how to use the `stellargraph` library to train a GraphSAGE model using one network and then use this trained model to predict node attributes for a different (but similar) network.\n",
    "\n",
    "Classification accuracy on the latter network was on par with classification accuracy on a set of training network test nodes with hidden labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/node-classification/graphsage-inductive-node-classification.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/node-classification/graphsage-inductive-node-classification.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
